基于训练数据和康复治疗数据构建神经网络模型,用于监测运动负载
1College of Physical Education, China Three Gorges University, Yichang, 443002, Hubei, China.
Heliyon
|July 11, 2024
概括
本研究介绍了训练,用于使用殖民地优化 (ACO) 神经网络监测运动员的运动负载. 结果显示,ACO神经网络准确地预测了运动负载,并证实了.
科学领域:
- 运动医学 运动医学
- 人工智能的人工智能
- 康复 康复 康复 康复
背景情况:
- 物联网 (IoT) 能够实现设备对设备的通信,推动医疗保健,智能城市和智能家居中的智能应用.
- 高性能计算和机器学习正在推动体育医学中的智能辅助系统.
- 对运动员有好处,包括改善体力,灵活性和情绪健康,帮助体育伤害康复.
研究的目的:
- 探索一种新的方法来监测运动员的运动负载,并通过训练帮助康复.
- 使用殖民地优化 (ACO) 神经网络技术开发运动负载监控模型.
- 为了验证训练在减轻运动后疲劳方面的有效性.
主要方法:
- 利用神经网络技术超越传统的基于经验的培训方法.
- 通过实验性炼研究收集和分析训练数据.
- 开发了一个运动负载监控模型,采用殖民地优化 (ACO) 神经网络.
主要成果:
- 殖民地优化 (ACO) 神经网络模型表现出高预测准确度.
- 该模型的预测值为9.106,与9.109.9的实际检测值相比,最小误差为-0.003.
- 证实了训练对减少运动后疲劳的显著影响.
结论:
- ACO神经网络模型有效地捕捉了训练强度和运动负载之间的关系.
- 这种方法提供了一种新的,准确的方法来监测运动员的运动负载并支持康复.
- 高性能计算系统的整合标志着体育和健康技术的重大进步.
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